What is the difference between identity analytics and UEBA?

They overlap but focus differently. UEBA (user and entity behavior analytics) is a security-operations tool that models runtime behavior to detect active threats. Identity analytics focuses on the access layer itself — entitlements, roles, and usage — to improve governance decisions. UEBA asks "is this session suspicious"; identity analytics asks "should this person have this access at all".

Do you need machine learning for identity analytics?

No. Most of the value comes from straightforward analysis: comparing users to peers, flagging unused entitlements, and detecting rule violations like segregation of duties conflicts. Machine learning adds value later for subtle anomaly detection, but clean, correlated data matters far more than the algorithm.

Identity Analytics

What is Identity Analytics?

Identity analytics applies data analysis to identity and access information to surface risky entitlements, anomalies, and cleanup opportunities across systems.

Last updated: 15 July 2026

What identity analytics does

Identity analytics turns raw identity data — accounts, entitlements, group memberships, access requests, login activity — into answers. Instead of asking a reviewer to inspect thousands of rows, analytics ranks and flags: which users hold access far beyond their peers, which entitlements have not been used in months, which accounts show unusual activity patterns, and where segregation of duties conflicts exist.

Common techniques include peer-group analysis (comparing a user's access to colleagues with the same role), usage analysis (correlating held entitlements with actual activity), outlier detection, and risk scoring that combines factors like privilege level, data sensitivity, and account hygiene into a single comparable number.

Why identity analytics matters

The biggest failure mode of governance programs is rubber-stamping: reviewers facing hundreds of identical-looking line items approve everything. Analytics fixes the economics by focusing human attention where it matters — the outliers, the unused privileges, the high-risk combinations — and letting low-risk, well-explained access pass with lighter scrutiny.

Analytics also makes risk visible before it becomes an incident. Privilege creep, orphaned accounts, and toxic entitlement combinations all leave statistical fingerprints long before an attacker or auditor finds them.

Getting started with identity analytics

Analytics is only as good as its input, so the first step is consolidating identity data: correlating accounts to people across systems and normalizing entitlements into a common inventory. From there, start with high-value, low-complexity questions — unused privileged access, departed users with active accounts, peer-group outliers — before attempting sophisticated behavioral models.

Feed the findings into existing processes rather than a separate dashboard nobody checks: risk scores should prioritize access review campaigns, and anomalies should open tickets. Monosync provides the correlated identity and entitlement data this analysis depends on, along with reporting that highlights review-worthy access.

Frequently asked questions

What is the difference between identity analytics and UEBA?: They overlap but focus differently. UEBA (user and entity behavior analytics) is a security-operations tool that models runtime behavior to detect active threats. Identity analytics focuses on the access layer itself — entitlements, roles, and usage — to improve governance decisions. UEBA asks "is this session suspicious"; identity analytics asks "should this person have this access at all".
Do you need machine learning for identity analytics?: No. Most of the value comes from straightforward analysis: comparing users to peers, flagging unused entitlements, and detecting rule violations like segregation of duties conflicts. Machine learning adds value later for subtle anomaly detection, but clean, correlated data matters far more than the algorithm.